Level set methods for watershed image segmentation

  • Authors:

  • Xue-Cheng Tai;Erlend Hodneland;Joachim Weickert;Nickolay V. Bukoreshtliev;Arvid Lundervold;Hans-Hermann Gerdes

  • Affiliations:

  • Department of Mathematics, University of Bergen, Bergen, Norway;Department of Biomedicine, University of Bergen, Bergen, Norway;Faculty of Mathematics and Computer Science, Saarland University, Saarbrücken, Germany;Department of Biomedicine, University of Bergen, Bergen, Norway;Department of Biomedicine, University of Bergen, Bergen, Norway;Department of Biomedicine, University of Bergen, Bergen, Norway

  • Venue:
  • SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
  • Year:
  • 2007

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Abstract

In this work a marker-controlled and regularized watershed segmentation is proposed. Only a few previous studies address the task of regularizing the obtained watershed lines from the traditional marker-controlled watershed segmentation. In the present formulation, the topographical distance function is applied in a level set formulation to perform the segmentation, and the regularization is easily accomplished by regularizing the level set functions. Based on the well-known Four-Color theorem, a mathematical model is developed for the proposed ideas. With this model, it is possible to segment any 2D image with arbitrary number of phases with as few as one or two level set functions. The algorithm has been tested on real 2D fluorescence microscopy images displaying rat cancer cells, and the algorithm has also been compared to a standard watershed segmentation as it is implemented in MATLAB. For a fixed set of markers and a fixed set of challenging images, the comparison of these two methods shows that the present level set formulation performs better than a standard watershed segmentation.